Man woman detection in surveillance images

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Citations (Scopus)

Abstract

Human gender detection from body profile is an important task for surveillance. Most surveillance cameras are placed at a distance such that it is not possible to see people's face clearly. In this paper, we report the comparison between fast-feature pyramids and deep region-based convolutional neural network (RCNN) to detect a person in surveillance images. Since RCNN performs better in detecting a person, further training is applied to the RCNN to detect man and woman. Transfer learning strategy is used due to a small number of training images. The result shows that the trained RCNN can detect man and woman with promising result.

Original languageEnglish
Title of host publication2017 5th International Conference on Information and Communication Technology, ICoIC7 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509049127
DOIs
Publication statusPublished - 18 Oct 2017
Event5th International Conference on Information and Communication Technology, ICoIC7 2017 - Melaka, Malaysia
Duration: 17 May 201719 May 2017

Publication series

Name2017 5th International Conference on Information and Communication Technology, ICoIC7 2017

Conference

Conference5th International Conference on Information and Communication Technology, ICoIC7 2017
CountryMalaysia
CityMelaka
Period17/05/1719/05/17

Keywords

  • detection
  • fast feature pyramids
  • gender
  • man woman
  • region based CNN
  • transfer learning

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